Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations1418
Missing cells9936
Missing cells (%)21.2%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory1.4 MiB
Average record size in memory1018.2 B

Variable types

Categorical14
Unsupported8
Text2
Numeric8
Boolean1

Alerts

intentType has constant value "science" Constant
obs_collection has constant value "TESS" Constant
instrument_name has constant value "Photometer" Constant
project has constant value "TESS" Constant
filters has constant value "TESS" Constant
wavelength_region has constant value "Optical" Constant
target_name has constant value "TESS FFI" Constant
dataproduct_type has constant value "image" Constant
proposal_pi has constant value "Ricker, George" Constant
calib_level has constant value "3" Constant
dataRights has constant value "PUBLIC" Constant
mtFlag has constant value "False" Constant
Dataset has 1 (0.1%) duplicate rowsDuplicates
em_max is highly overall correlated with em_minHigh correlation
em_min is highly overall correlated with em_maxHigh correlation
objID is highly overall correlated with obsid and 5 other fieldsHigh correlation
obsid is highly overall correlated with objID and 5 other fieldsHigh correlation
sequence_number is highly overall correlated with objID and 5 other fieldsHigh correlation
t_exptime is highly overall correlated with objID and 5 other fieldsHigh correlation
t_max is highly overall correlated with objID and 5 other fieldsHigh correlation
t_min is highly overall correlated with objID and 5 other fieldsHigh correlation
t_obs_release is highly overall correlated with objID and 5 other fieldsHigh correlation
provenance_name is highly imbalanced (90.2%) Imbalance
em_min is highly imbalanced (89.7%) Imbalance
em_max is highly imbalanced (89.7%) Imbalance
target_classification has 1418 (100.0%) missing values Missing
obs_title has 1418 (100.0%) missing values Missing
proposal_id has 1418 (100.0%) missing values Missing
proposal_type has 1418 (100.0%) missing values Missing
jpegURL has 1418 (100.0%) missing values Missing
dataURL has 1418 (100.0%) missing values Missing
srcDen has 1418 (100.0%) missing values Missing
target_classification is an unsupported type, check if it needs cleaning or further analysis Unsupported
s_ra is an unsupported type, check if it needs cleaning or further analysis Unsupported
obs_title is an unsupported type, check if it needs cleaning or further analysis Unsupported
proposal_id is an unsupported type, check if it needs cleaning or further analysis Unsupported
proposal_type is an unsupported type, check if it needs cleaning or further analysis Unsupported
jpegURL is an unsupported type, check if it needs cleaning or further analysis Unsupported
dataURL is an unsupported type, check if it needs cleaning or further analysis Unsupported
srcDen is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-05-25 14:34:16.731387
Analysis finished2025-05-25 14:34:25.765207
Duration9.03 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

intentType
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
science
1418 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters9926
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowscience
2nd rowscience
3rd rowscience
4th rowscience
5th rowscience

Common Values

ValueCountFrequency (%)
science 1418
100.0%

Length

2025-05-25T15:34:25.892905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:25.951708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
science 1418
100.0%

Most occurring characters

ValueCountFrequency (%)
c 2836
28.6%
e 2836
28.6%
s 1418
14.3%
i 1418
14.3%
n 1418
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9926
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 2836
28.6%
e 2836
28.6%
s 1418
14.3%
i 1418
14.3%
n 1418
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9926
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 2836
28.6%
e 2836
28.6%
s 1418
14.3%
i 1418
14.3%
n 1418
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9926
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 2836
28.6%
e 2836
28.6%
s 1418
14.3%
i 1418
14.3%
n 1418
14.3%

obs_collection
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
TESS
1418 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5672
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTESS
2nd rowTESS
3rd rowTESS
4th rowTESS
5th rowTESS

Common Values

ValueCountFrequency (%)
TESS 1418
100.0%

Length

2025-05-25T15:34:26.030499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:26.095326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tess 1418
100.0%

Most occurring characters

ValueCountFrequency (%)
S 2836
50.0%
T 1418
25.0%
E 1418
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 2836
50.0%
T 1418
25.0%
E 1418
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 2836
50.0%
T 1418
25.0%
E 1418
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 2836
50.0%
T 1418
25.0%
E 1418
25.0%

provenance_name
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
SPOC
1400 
SPOa
 
18

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5672
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSPOC
2nd rowSPOa
3rd rowSPOa
4th rowSPOa
5th rowSPOa

Common Values

ValueCountFrequency (%)
SPOC 1400
98.7%
SPOa 18
 
1.3%

Length

2025-05-25T15:34:26.172120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:26.236972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
spoc 1400
98.7%
spoa 18
 
1.3%

Most occurring characters

ValueCountFrequency (%)
S 1418
25.0%
P 1418
25.0%
O 1418
25.0%
C 1400
24.7%
a 18
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1418
25.0%
P 1418
25.0%
O 1418
25.0%
C 1400
24.7%
a 18
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1418
25.0%
P 1418
25.0%
O 1418
25.0%
C 1400
24.7%
a 18
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1418
25.0%
P 1418
25.0%
O 1418
25.0%
C 1400
24.7%
a 18
 
0.3%

instrument_name
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size81.8 KiB
Photometer
1418 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters14180
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPhotometer
2nd rowPhotometer
3rd rowPhotometer
4th rowPhotometer
5th rowPhotometer

Common Values

ValueCountFrequency (%)
Photometer 1418
100.0%

Length

2025-05-25T15:34:26.319763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:26.385587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
photometer 1418
100.0%

Most occurring characters

ValueCountFrequency (%)
o 2836
20.0%
e 2836
20.0%
t 2836
20.0%
h 1418
10.0%
P 1418
10.0%
m 1418
10.0%
r 1418
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2836
20.0%
e 2836
20.0%
t 2836
20.0%
h 1418
10.0%
P 1418
10.0%
m 1418
10.0%
r 1418
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2836
20.0%
e 2836
20.0%
t 2836
20.0%
h 1418
10.0%
P 1418
10.0%
m 1418
10.0%
r 1418
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2836
20.0%
e 2836
20.0%
t 2836
20.0%
h 1418
10.0%
P 1418
10.0%
m 1418
10.0%
r 1418
10.0%

project
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
TESS
1418 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5672
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTESS
2nd rowTESS
3rd rowTESS
4th rowTESS
5th rowTESS

Common Values

ValueCountFrequency (%)
TESS 1418
100.0%

Length

2025-05-25T15:34:26.460373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:26.522214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tess 1418
100.0%

Most occurring characters

ValueCountFrequency (%)
S 2836
50.0%
T 1418
25.0%
E 1418
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 2836
50.0%
T 1418
25.0%
E 1418
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 2836
50.0%
T 1418
25.0%
E 1418
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 2836
50.0%
T 1418
25.0%
E 1418
25.0%

filters
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.5 KiB
TESS
1418 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5672
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTESS
2nd rowTESS
3rd rowTESS
4th rowTESS
5th rowTESS

Common Values

ValueCountFrequency (%)
TESS 1418
100.0%

Length

2025-05-25T15:34:26.598024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:26.658843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tess 1418
100.0%

Most occurring characters

ValueCountFrequency (%)
S 2836
50.0%
T 1418
25.0%
E 1418
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 2836
50.0%
T 1418
25.0%
E 1418
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 2836
50.0%
T 1418
25.0%
E 1418
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 2836
50.0%
T 1418
25.0%
E 1418
25.0%

wavelength_region
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.7 KiB
Optical
1418 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters9926
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOptical
2nd rowOptical
3rd rowOptical
4th rowOptical
5th rowOptical

Common Values

ValueCountFrequency (%)
Optical 1418
100.0%

Length

2025-05-25T15:34:26.733643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:26.792486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
optical 1418
100.0%

Most occurring characters

ValueCountFrequency (%)
O 1418
14.3%
p 1418
14.3%
t 1418
14.3%
i 1418
14.3%
c 1418
14.3%
a 1418
14.3%
l 1418
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9926
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1418
14.3%
p 1418
14.3%
t 1418
14.3%
i 1418
14.3%
c 1418
14.3%
a 1418
14.3%
l 1418
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9926
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1418
14.3%
p 1418
14.3%
t 1418
14.3%
i 1418
14.3%
c 1418
14.3%
a 1418
14.3%
l 1418
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9926
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1418
14.3%
p 1418
14.3%
t 1418
14.3%
i 1418
14.3%
c 1418
14.3%
a 1418
14.3%
l 1418
14.3%

target_name
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.1 KiB
TESS FFI
1418 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters11344
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTESS FFI
2nd rowTESS FFI
3rd rowTESS FFI
4th rowTESS FFI
5th rowTESS FFI

Common Values

ValueCountFrequency (%)
TESS FFI 1418
100.0%

Length

2025-05-25T15:34:26.871276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:26.945054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
tess 1418
50.0%
ffi 1418
50.0%

Most occurring characters

ValueCountFrequency (%)
F 2836
25.0%
S 2836
25.0%
E 1418
12.5%
T 1418
12.5%
1418
12.5%
I 1418
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11344
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 2836
25.0%
S 2836
25.0%
E 1418
12.5%
T 1418
12.5%
1418
12.5%
I 1418
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11344
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 2836
25.0%
S 2836
25.0%
E 1418
12.5%
T 1418
12.5%
1418
12.5%
I 1418
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11344
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 2836
25.0%
S 2836
25.0%
E 1418
12.5%
T 1418
12.5%
1418
12.5%
I 1418
12.5%

target_classification
Unsupported

Missing  Rejected  Unsupported 

Missing1418
Missing (%)100.0%
Memory size11.2 KiB

obs_id
Text

Distinct1408
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size87.4 KiB
2025-05-25T15:34:27.270183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters19852
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1407 ?
Unique (%)99.2%

Sample

1st rowtess-s0086-1-1
2nd rowtess-s0086-1-2
3rd rowtess-s0086-1-3
4th rowtess-s0086-1-4
5th rowtess-s0086-2-1
ValueCountFrequency (%)
tess-s0086-1-3 11
 
0.8%
tess-s0060-2-3 1
 
0.1%
tess-s0060-2-4 1
 
0.1%
tess-s0060-3-1 1
 
0.1%
tess-s0060-3-2 1
 
0.1%
tess-s0060-3-3 1
 
0.1%
tess-s0060-3-4 1
 
0.1%
tess-s0060-4-1 1
 
0.1%
tess-s0060-4-2 1
 
0.1%
tess-s0060-4-3 1
 
0.1%
Other values (1398) 1398
98.6%
2025-05-25T15:34:27.673116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 4254
21.4%
s 4254
21.4%
0 3108
15.7%
t 1418
 
7.1%
e 1418
 
7.1%
1 1018
 
5.1%
3 1018
 
5.1%
4 1008
 
5.1%
2 1008
 
5.1%
6 314
 
1.6%
Other values (4) 1034
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19852
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 4254
21.4%
s 4254
21.4%
0 3108
15.7%
t 1418
 
7.1%
e 1418
 
7.1%
1 1018
 
5.1%
3 1018
 
5.1%
4 1008
 
5.1%
2 1008
 
5.1%
6 314
 
1.6%
Other values (4) 1034
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19852
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 4254
21.4%
s 4254
21.4%
0 3108
15.7%
t 1418
 
7.1%
e 1418
 
7.1%
1 1018
 
5.1%
3 1018
 
5.1%
4 1008
 
5.1%
2 1008
 
5.1%
6 314
 
1.6%
Other values (4) 1034
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19852
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 4254
21.4%
s 4254
21.4%
0 3108
15.7%
t 1418
 
7.1%
e 1418
 
7.1%
1 1018
 
5.1%
3 1018
 
5.1%
4 1008
 
5.1%
2 1008
 
5.1%
6 314
 
1.6%
Other values (4) 1034
 
5.2%

s_ra
Unsupported

Rejected  Unsupported 

Missing10
Missing (%)0.7%
Memory size45.2 KiB

s_dec
Real number (ℝ)

Distinct1408
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5406487
Minimum-85.380468
Maximum89.118477
Zeros0
Zeros (%)0.0%
Negative594
Negative (%)41.9%
Memory size11.2 KiB
2025-05-25T15:34:27.788798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-85.380468
5-th percentile-72.070598
Q1-42.829386
median16.712283
Q355.599538
95-th percentile74.998246
Maximum89.118477
Range174.49895
Interquartile range (IQR)98.428924

Descriptive statistics

Standard deviation51.121722
Coefficient of variation (CV)6.779486
Kurtosis-1.3673705
Mean7.5406487
Median Absolute Deviation (MAD)44.879624
Skewness-0.2289824
Sum10692.64
Variance2613.4305
MonotonicityNot monotonic
2025-05-25T15:34:27.926454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.4220995 11
 
0.8%
6.364932208 1
 
0.1%
-28.15278596 1
 
0.1%
-31.53194004 1
 
0.1%
-19.77690812 1
 
0.1%
-16.69775904 1
 
0.1%
-43.11398233 1
 
0.1%
-39.29575131 1
 
0.1%
59.74779106 1
 
0.1%
56.24287205 1
 
0.1%
Other values (1398) 1398
98.6%
ValueCountFrequency (%)
-85.38046835 1
0.1%
-84.89110863 1
0.1%
-84.59901003 1
0.1%
-84.54862806 1
0.1%
-83.55804826 1
0.1%
-83.55562886 1
0.1%
-82.87129237 1
0.1%
-82.86128257 1
0.1%
-82.82419625 1
0.1%
-82.66146248 1
0.1%
ValueCountFrequency (%)
89.11847737 1
0.1%
87.49107515 1
0.1%
86.68064422 1
0.1%
85.34990518 1
0.1%
85.33815322 1
0.1%
85.27337778 1
0.1%
85.2189659 1
0.1%
85.17739266 1
0.1%
84.69099321 1
0.1%
84.50840672 1
0.1%

dataproduct_type
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size74.9 KiB
image
1418 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters7090
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowimage
2nd rowimage
3rd rowimage
4th rowimage
5th rowimage

Common Values

ValueCountFrequency (%)
image 1418
100.0%

Length

2025-05-25T15:34:28.051121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:28.115924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
image 1418
100.0%

Most occurring characters

ValueCountFrequency (%)
i 1418
20.0%
m 1418
20.0%
a 1418
20.0%
g 1418
20.0%
e 1418
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1418
20.0%
m 1418
20.0%
a 1418
20.0%
g 1418
20.0%
e 1418
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1418
20.0%
m 1418
20.0%
a 1418
20.0%
g 1418
20.0%
e 1418
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1418
20.0%
m 1418
20.0%
a 1418
20.0%
g 1418
20.0%
e 1418
20.0%

proposal_pi
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size87.4 KiB
Ricker, George
1418 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters19852
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRicker, George
2nd rowRicker, George
3rd rowRicker, George
4th rowRicker, George
5th rowRicker, George

Common Values

ValueCountFrequency (%)
Ricker, George 1418
100.0%

Length

2025-05-25T15:34:28.195747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:28.254551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ricker 1418
50.0%
george 1418
50.0%

Most occurring characters

ValueCountFrequency (%)
e 4254
21.4%
r 2836
14.3%
R 1418
 
7.1%
c 1418
 
7.1%
i 1418
 
7.1%
k 1418
 
7.1%
, 1418
 
7.1%
1418
 
7.1%
G 1418
 
7.1%
o 1418
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19852
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4254
21.4%
r 2836
14.3%
R 1418
 
7.1%
c 1418
 
7.1%
i 1418
 
7.1%
k 1418
 
7.1%
, 1418
 
7.1%
1418
 
7.1%
G 1418
 
7.1%
o 1418
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19852
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4254
21.4%
r 2836
14.3%
R 1418
 
7.1%
c 1418
 
7.1%
i 1418
 
7.1%
k 1418
 
7.1%
, 1418
 
7.1%
1418
 
7.1%
G 1418
 
7.1%
o 1418
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19852
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4254
21.4%
r 2836
14.3%
R 1418
 
7.1%
c 1418
 
7.1%
i 1418
 
7.1%
k 1418
 
7.1%
, 1418
 
7.1%
1418
 
7.1%
G 1418
 
7.1%
o 1418
 
7.1%

calib_level
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size69.4 KiB
3
1418 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1418
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1418
100.0%

Length

2025-05-25T15:34:28.330350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:28.392225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 1418
100.0%

Most occurring characters

ValueCountFrequency (%)
3 1418
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1418
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1418
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1418
100.0%

t_min
Real number (ℝ)

High correlation 

Distinct1408
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59519.739
Minimum58324.812
Maximum60689.651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-05-25T15:34:28.476982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum58324.812
5-th percentile58437.482
Q158927.604
median59525.005
Q360126.138
95-th percentile60610.049
Maximum60689.651
Range2364.8394
Interquartile range (IQR)1198.5341

Descriptive statistics

Standard deviation695.52092
Coefficient of variation (CV)0.011685551
Kurtosis-1.2081074
Mean59519.739
Median Absolute Deviation (MAD)601.132
Skewness-0.0080714243
Sum84398989
Variance483749.34
MonotonicityNot monotonic
2025-05-25T15:34:28.611636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60635.75979 11
 
0.8%
60689.65096 1
 
0.1%
60689.64917 1
 
0.1%
60689.64886 1
 
0.1%
60689.64969 1
 
0.1%
60689.65 1
 
0.1%
60689.64795 1
 
0.1%
60689.64825 1
 
0.1%
59936.39663 1
 
0.1%
60610.04772 1
 
0.1%
Other values (1398) 1398
98.6%
ValueCountFrequency (%)
58324.81152 1
0.1%
58324.81181 1
0.1%
58324.81276 1
0.1%
58324.81304 1
0.1%
58324.81386 1
0.1%
58324.81414 1
0.1%
58324.81499 1
0.1%
58324.81527 1
0.1%
58324.81591 1
0.1%
58324.8162 1
0.1%
ValueCountFrequency (%)
60689.65096 1
0.1%
60689.65065 1
0.1%
60689.65055 1
0.1%
60689.65025 1
0.1%
60689.65 1
0.1%
60689.64969 1
0.1%
60689.64917 1
0.1%
60689.64886 1
0.1%
60689.64825 1
0.1%
60689.64795 1
0.1%

t_max
Real number (ℝ)

High correlation 

Distinct1408
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59545.889
Minimum58352.666
Maximum60717.429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-05-25T15:34:28.894841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum58352.666
5-th percentile58463.773
Q158954.375
median59550.126
Q360153.898
95-th percentile60635.544
Maximum60717.429
Range2364.7631
Interquartile range (IQR)1199.5226

Descriptive statistics

Standard deviation695.61495
Coefficient of variation (CV)0.011681998
Kurtosis-1.2089736
Mean59545.889
Median Absolute Deviation (MAD)603.77074
Skewness-0.0082725356
Sum84436071
Variance483880.16
MonotonicityNot monotonic
2025-05-25T15:34:29.033470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60662.33121 11
 
0.8%
60717.42847 1
 
0.1%
60717.42669 1
 
0.1%
60717.42699 1
 
0.1%
60717.42782 1
 
0.1%
60717.42752 1
 
0.1%
60717.42607 1
 
0.1%
60717.42577 1
 
0.1%
59962.08149 1
 
0.1%
60635.54277 1
 
0.1%
Other values (1398) 1398
98.6%
ValueCountFrequency (%)
58352.66567 1
0.1%
58352.66598 1
0.1%
58352.6669 1
0.1%
58352.66721 1
0.1%
58352.66799 1
0.1%
58352.6683 1
0.1%
58352.66912 1
0.1%
58352.66942 1
0.1%
58352.67004 1
0.1%
58352.67034 1
0.1%
ValueCountFrequency (%)
60717.42877 1
0.1%
60717.42847 1
0.1%
60717.42837 1
0.1%
60717.42807 1
0.1%
60717.42782 1
0.1%
60717.42752 1
0.1%
60717.42699 1
0.1%
60717.42669 1
0.1%
60717.42607 1
0.1%
60717.42577 1
0.1%

t_exptime
Real number (ℝ)

High correlation 

Distinct44
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean633.82314
Minimum158.39992
Maximum1425.5994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-05-25T15:34:29.166139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum158.39992
5-th percentile158.39992
Q1158.39993
median475.19979
Q31425.5994
95-th percentile1425.5994
Maximum1425.5994
Range1267.1995
Interquartile range (IQR)1267.1994

Descriptive statistics

Standard deviation527.35471
Coefficient of variation (CV)0.83202186
Kurtosis-1.2536391
Mean633.82314
Median Absolute Deviation (MAD)316.79986
Skewness0.70751912
Sum898761.21
Variance278102.99
MonotonicityNot monotonic
2025-05-25T15:34:29.307742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
158.399927 208
 
14.7%
158.399928 128
 
9.0%
475.19979 64
 
4.5%
158.399924 64
 
4.5%
475.199789 64
 
4.5%
158.399926 58
 
4.1%
475.199788 48
 
3.4%
475.199787 48
 
3.4%
1425.599392 32
 
2.3%
475.199791 32
 
2.3%
Other values (34) 672
47.4%
ValueCountFrequency (%)
158.399923 16
 
1.1%
158.399924 64
 
4.5%
158.399925 32
 
2.3%
158.399926 58
 
4.1%
158.399927 208
14.7%
158.399928 128
9.0%
158.399929 16
 
1.1%
158.39993 16
 
1.1%
475.199767 16
 
1.1%
475.199775 16
 
1.1%
ValueCountFrequency (%)
1425.599438 16
1.1%
1425.599428 16
1.1%
1425.599424 16
1.1%
1425.599419 16
1.1%
1425.599416 16
1.1%
1425.599414 16
1.1%
1425.59941 16
1.1%
1425.599406 32
2.3%
1425.599402 32
2.3%
1425.599399 16
1.1%

em_min
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size74.9 KiB
600.0
1399 
1000.0
 
19

Length

Max length6
Median length5
Mean length5.0133992
Min length5

Characters and Unicode

Total characters7109
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row600.0
2nd row600.0
3rd row600.0
4th row600.0
5th row600.0

Common Values

ValueCountFrequency (%)
600.0 1399
98.7%
1000.0 19
 
1.3%

Length

2025-05-25T15:34:29.431430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:29.499225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
600.0 1399
98.7%
1000.0 19
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 4273
60.1%
. 1418
 
19.9%
6 1399
 
19.7%
1 19
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7109
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4273
60.1%
. 1418
 
19.9%
6 1399
 
19.7%
1 19
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7109
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4273
60.1%
. 1418
 
19.9%
6 1399
 
19.7%
1 19
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7109
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4273
60.1%
. 1418
 
19.9%
6 1399
 
19.7%
1 19
 
0.3%

em_max
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size76.3 KiB
1000.0
1399 
600.0
 
19

Length

Max length6
Median length6
Mean length5.9866008
Min length5

Characters and Unicode

Total characters8489
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000.0
2nd row1000.0
3rd row1000.0
4th row1000.0
5th row1000.0

Common Values

ValueCountFrequency (%)
1000.0 1399
98.7%
600.0 19
 
1.3%

Length

2025-05-25T15:34:29.586018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:29.654833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1000.0 1399
98.7%
600.0 19
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 5653
66.6%
. 1418
 
16.7%
1 1399
 
16.5%
6 19
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5653
66.6%
. 1418
 
16.7%
1 1399
 
16.5%
6 19
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5653
66.6%
. 1418
 
16.7%
1 1399
 
16.5%
6 19
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5653
66.6%
. 1418
 
16.7%
1 1399
 
16.5%
6 19
 
0.2%

obs_title
Unsupported

Missing  Rejected  Unsupported 

Missing1418
Missing (%)100.0%
Memory size11.2 KiB

t_obs_release
Real number (ℝ)

High correlation 

Distinct86
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59578.644
Minimum58458.583
Maximum60734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-05-25T15:34:29.744606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum58458.583
5-th percentile58514.667
Q158973
median59575
Q360177
95-th percentile60662
Maximum60734
Range2275.4167
Interquartile range (IQR)1204

Descriptive statistics

Standard deviation691.16666
Coefficient of variation (CV)0.011600913
Kurtosis-1.2233857
Mean59578.644
Median Absolute Deviation (MAD)602
Skewness0.022774877
Sum84482517
Variance477711.35
MonotonicityNot monotonic
2025-05-25T15:34:29.882225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58507.66667 32
 
2.3%
58458.58333 32
 
2.3%
60697 26
 
1.8%
58917 16
 
1.1%
58893 16
 
1.1%
59999 16
 
1.1%
60719 16
 
1.1%
59001 16
 
1.1%
58739.33333 16
 
1.1%
58756.33333 16
 
1.1%
Other values (76) 1216
85.8%
ValueCountFrequency (%)
58458.58333 32
2.3%
58507.66667 32
2.3%
58514.66667 16
1.1%
58541.83333 16
1.1%
58553.5 16
1.1%
58584.5 16
1.1%
58609.33333 16
1.1%
58635.33333 16
1.1%
58651.5 16
1.1%
58673.5 16
1.1%
ValueCountFrequency (%)
60734 16
1.1%
60719 16
1.1%
60697 26
1.8%
60662 16
1.1%
60654 16
1.1%
60635 16
1.1%
60613 16
1.1%
60573 16
1.1%
60545 16
1.1%
60515 16
1.1%

proposal_id
Unsupported

Missing  Rejected  Unsupported 

Missing1418
Missing (%)100.0%
Memory size11.2 KiB

proposal_type
Unsupported

Missing  Rejected  Unsupported 

Missing1418
Missing (%)100.0%
Memory size11.2 KiB

sequence_number
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.792666
Minimum1
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-05-25T15:34:30.018834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q123
median45
Q367
95-th percentile85
Maximum88
Range87
Interquartile range (IQR)44

Descriptive statistics

Standard deviation25.558173
Coefficient of variation (CV)0.57058835
Kurtosis-1.205405
Mean44.792666
Median Absolute Deviation (MAD)22
Skewness-0.0041478601
Sum63516
Variance653.22021
MonotonicityNot monotonic
2025-05-25T15:34:30.149486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 26
 
1.8%
61 16
 
1.1%
20 16
 
1.1%
21 16
 
1.1%
22 16
 
1.1%
23 16
 
1.1%
87 16
 
1.1%
24 16
 
1.1%
14 16
 
1.1%
15 16
 
1.1%
Other values (78) 1248
88.0%
ValueCountFrequency (%)
1 16
1.1%
2 16
1.1%
3 16
1.1%
4 16
1.1%
5 16
1.1%
6 16
1.1%
7 16
1.1%
8 16
1.1%
9 16
1.1%
10 16
1.1%
ValueCountFrequency (%)
88 16
1.1%
87 16
1.1%
86 26
1.8%
85 16
1.1%
84 16
1.1%
83 16
1.1%
82 16
1.1%
81 16
1.1%
80 16
1.1%
79 16
1.1%
Distinct1408
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size229.3 KiB
2025-05-25T15:34:30.405802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length137
Median length133
Mean length116.50564
Min length100

Characters and Unicode

Total characters165205
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1407 ?
Unique (%)99.2%

Sample

1st rowPOLYGON 84.397259 54.220049 84.340478 42.731886 67.849991 41.751568 64.359824 53.425924 84.397259 54.220049
2nd rowPOLYGON 64.081165 53.379423 67.62671 41.711452 52.195423 38.463737 46.379456 49.163669 64.081165 53.379423
3rd rowPOLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355
4th rowPOLYGON 70.29962 29.735295 67.909289 41.484483 84.326369 42.4735 83.925435 30.988125 70.29962 29.735295
5th rowPOLYGON 95.427814 77.267546 87.798089 66.012517 58.089341 65.068322 42.982691 75.947124 95.427814 77.267546
ValueCountFrequency (%)
polygon 1418
 
9.1%
56.900467 22
 
0.1%
27.410355 22
 
0.1%
52.298245 11
 
0.1%
38.232035 11
 
0.1%
67.682461 11
 
0.1%
41.457243 11
 
0.1%
70.101446 11
 
0.1%
29.709816 11
 
0.1%
50.95751200 2
 
< 0.1%
Other values (11254) 14068
90.2%
2025-05-25T15:34:30.794761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16905
10.2%
14180
8.6%
. 14180
8.6%
2 13406
 
8.1%
1 13143
 
8.0%
3 12458
 
7.5%
6 12052
 
7.3%
5 11888
 
7.2%
7 11372
 
6.9%
4 11319
 
6.9%
Other values (9) 34302
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 165205
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 16905
10.2%
14180
8.6%
. 14180
8.6%
2 13406
 
8.1%
1 13143
 
8.0%
3 12458
 
7.5%
6 12052
 
7.3%
5 11888
 
7.2%
7 11372
 
6.9%
4 11319
 
6.9%
Other values (9) 34302
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 165205
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 16905
10.2%
14180
8.6%
. 14180
8.6%
2 13406
 
8.1%
1 13143
 
8.0%
3 12458
 
7.5%
6 12052
 
7.3%
5 11888
 
7.2%
7 11372
 
6.9%
4 11319
 
6.9%
Other values (9) 34302
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 165205
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 16905
10.2%
14180
8.6%
. 14180
8.6%
2 13406
 
8.1%
1 13143
 
8.0%
3 12458
 
7.5%
6 12052
 
7.3%
5 11888
 
7.2%
7 11372
 
6.9%
4 11319
 
6.9%
Other values (9) 34302
20.8%

jpegURL
Unsupported

Missing  Rejected  Unsupported 

Missing1418
Missing (%)100.0%
Memory size11.2 KiB

dataURL
Unsupported

Missing  Rejected  Unsupported 

Missing1418
Missing (%)100.0%
Memory size11.2 KiB

dataRights
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size76.3 KiB
PUBLIC
1418 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8508
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPUBLIC
2nd rowPUBLIC
3rd rowPUBLIC
4th rowPUBLIC
5th rowPUBLIC

Common Values

ValueCountFrequency (%)
PUBLIC 1418
100.0%

Length

2025-05-25T15:34:30.898484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T15:34:30.959347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
public 1418
100.0%

Most occurring characters

ValueCountFrequency (%)
P 1418
16.7%
U 1418
16.7%
B 1418
16.7%
L 1418
16.7%
I 1418
16.7%
C 1418
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8508
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 1418
16.7%
U 1418
16.7%
B 1418
16.7%
L 1418
16.7%
I 1418
16.7%
C 1418
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8508
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 1418
16.7%
U 1418
16.7%
B 1418
16.7%
L 1418
16.7%
I 1418
16.7%
C 1418
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8508
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 1418
16.7%
U 1418
16.7%
B 1418
16.7%
L 1418
16.7%
I 1418
16.7%
C 1418
16.7%

mtFlag
Boolean

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
False
1418 
ValueCountFrequency (%)
False 1418
100.0%
2025-05-25T15:34:30.992232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

srcDen
Unsupported

Missing  Rejected  Unsupported 

Missing1418
Missing (%)100.0%
Memory size11.2 KiB

obsid
Real number (ℝ)

High correlation 

Distinct1408
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0386505 × 108
Minimum27266910
Maximum2.4792257 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-05-25T15:34:31.077030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27266910
5-th percentile27428568
Q160827598
median71308552
Q31.7236892 × 108
95-th percentile2.3392959 × 108
Maximum2.4792257 × 108
Range2.2065566 × 108
Interquartile range (IQR)1.1154132 × 108

Descriptive statistics

Standard deviation73139218
Coefficient of variation (CV)0.70417545
Kurtosis-0.95823351
Mean1.0386505 × 108
Median Absolute Deviation (MAD)43464072
Skewness0.7258232
Sum1.4728064 × 1011
Variance5.3493453 × 1015
MonotonicityNot monotonic
2025-05-25T15:34:31.219623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
236870242 11
 
0.8%
247626217 1
 
0.1%
247628587 1
 
0.1%
247630902 1
 
0.1%
247898034 1
 
0.1%
247900391 1
 
0.1%
247902682 1
 
0.1%
247904946 1
 
0.1%
115344219 1
 
0.1%
233942808 1
 
0.1%
Other values (1398) 1398
98.6%
ValueCountFrequency (%)
27266910 1
0.1%
27266911 1
0.1%
27266912 1
0.1%
27266913 1
0.1%
27266914 1
0.1%
27266915 1
0.1%
27266916 1
0.1%
27266917 1
0.1%
27266918 1
0.1%
27266919 1
0.1%
ValueCountFrequency (%)
247922567 1
0.1%
247919459 1
0.1%
247916479 1
0.1%
247913721 1
0.1%
247910493 1
0.1%
247907669 1
0.1%
247904946 1
0.1%
247902682 1
0.1%
247900391 1
0.1%
247898034 1
0.1%

objID
Real number (ℝ)

High correlation 

Distinct1408
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2704342 × 108
Minimum70120107
Maximum7.3209552 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2025-05-25T15:34:31.342296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum70120107
5-th percentile70441402
Q11.0915561 × 108
median1.3768408 × 108
Q33.0219586 × 108
95-th percentile6.7349535 × 108
Maximum7.3209552 × 108
Range6.6197541 × 108
Interquartile range (IQR)1.9304025 × 108

Descriptive statistics

Standard deviation1.9161853 × 108
Coefficient of variation (CV)0.84397309
Kurtosis0.73618956
Mean2.2704342 × 108
Median Absolute Deviation (MAD)66511983
Skewness1.4038058
Sum3.2194757 × 1011
Variance3.6717663 × 1016
MonotonicityNot monotonic
2025-05-25T15:34:31.478930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700713800 11
 
0.8%
732006359 1
 
0.1%
732013939 1
 
0.1%
732021757 1
 
0.1%
732028075 1
 
0.1%
732032390 1
 
0.1%
732038577 1
 
0.1%
732045432 1
 
0.1%
213378251 1
 
0.1%
673578297 1
 
0.1%
Other values (1398) 1398
98.6%
ValueCountFrequency (%)
70120107 1
0.1%
70120108 1
0.1%
70120109 1
0.1%
70120110 1
0.1%
70120111 1
0.1%
70120112 1
0.1%
70120113 1
0.1%
70120114 1
0.1%
70120115 1
0.1%
70120116 1
0.1%
ValueCountFrequency (%)
732095520 1
0.1%
732085054 1
0.1%
732076216 1
0.1%
732067225 1
0.1%
732056593 1
0.1%
732049844 1
0.1%
732045432 1
0.1%
732038577 1
0.1%
732032390 1
0.1%
732028075 1
0.1%

Interactions

2025-05-25T15:34:24.207373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:17.410548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:18.307148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:19.256609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:20.121298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:21.076745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:22.103998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:23.241954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:24.328051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:17.519254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:18.412865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:19.353353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:20.244967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:21.188445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:22.238636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:23.351662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:24.451720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:17.632955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:18.540524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:19.471036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:20.358665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:21.312116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:22.517891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:23.469347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:24.565415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:17.738670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:18.735004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:19.563789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:20.473356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:21.431794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:22.722343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:23.570076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:24.683102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:17.845383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:18.846708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:19.665517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:20.582067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:21.553470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:22.826077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:23.674798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:24.783833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:17.948108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:18.953419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:19.766246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:20.694772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:21.667165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:22.923822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:23.786499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:24.956370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:18.058813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:19.050163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:19.889917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:20.852343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:21.777871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:23.024537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:23.900208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:25.060095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:18.204423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:19.149920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:20.002617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:20.963048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:21.883586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:23.135244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-25T15:34:23.998930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-25T15:34:31.575672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
em_maxem_minobjIDobsidprovenance_names_decsequence_numbert_exptimet_maxt_mint_obs_release
em_max1.0000.9730.2930.3700.1210.1010.2300.1440.2300.2300.197
em_min0.9731.0000.2930.3700.1210.1010.2300.1440.2300.2300.197
objID0.2930.2931.0001.0000.4290.0940.883-0.8360.8830.8830.883
obsid0.3700.3701.0001.0000.3150.0940.883-0.8360.8830.8830.883
provenance_name0.1210.1210.4290.3151.0000.1870.3150.1400.3150.3150.281
s_dec0.1010.1010.0940.0940.1871.0000.249-0.1760.2480.2480.249
sequence_number0.2300.2300.8830.8830.3150.2491.000-0.9511.0001.0001.000
t_exptime0.1440.144-0.836-0.8360.140-0.176-0.9511.000-0.951-0.951-0.951
t_max0.2300.2300.8830.8830.3150.2481.000-0.9511.0001.0001.000
t_min0.2300.2300.8830.8830.3150.2481.000-0.9511.0001.0001.000
t_obs_release0.1970.1970.8830.8830.2810.2491.000-0.9511.0001.0001.000

Missing values

2025-05-25T15:34:25.300480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-25T15:34:25.610660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

intentTypeobs_collectionprovenance_nameinstrument_nameprojectfilterswavelength_regiontarget_nametarget_classificationobs_ids_ras_decdataproduct_typeproposal_picalib_levelt_mint_maxt_exptimeem_minem_maxobs_titlet_obs_releaseproposal_idproposal_typesequence_numbers_regionjpegURLdataURLdataRightsmtFlagsrcDenobsidobjID
0scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-175.25737948.394182imageRicker, George360635.75911760662.331107158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 84.397259 54.220049 84.340478 42.731886 67.849991 41.751568 64.359824 53.425924 84.397259 54.220049NaNNaNPUBLICFalseNaN236870228700694124
1scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-257.52319146.001910imageRicker, George360635.75939860662.330808158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 64.081165 53.379423 67.62671 41.711452 52.195423 38.463737 46.379456 49.163669 64.081165 53.379423NaNNaNPUBLICFalseNaN236870229700703621
2scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-361.74244134.422100imageRicker, George360635.75979360662.331209158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355NaNNaNPUBLICFalseNaN236870242700713800
3scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-476.59971736.405754imageRicker, George360635.75951360662.331509158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 70.29962 29.735295 67.909289 41.484483 84.326369 42.4735 83.925435 30.988125 70.29962 29.735295NaNNaNPUBLICFalseNaN236870243700721420
4scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-2-171.01834572.117377imageRicker, George360635.75775060662.329734158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 95.427814 77.267546 87.798089 66.012517 58.089341 65.068322 42.982691 75.947124 95.427814 77.267546NaNNaNPUBLICFalseNaN236870245700730556
5scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-2-237.5040167.492252imageRicker, George360635.75802660662.329431158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 42.356322 75.86303 57.677915 65.022354 34.630883 59.087104 16.286344 67.150322 42.356322 75.86303NaNNaNPUBLICFalseNaN236870248700740843
6scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-2-350.66781957.064990imageRicker, George360635.75884960662.330253158.3999261000.0600.0NaN60697.0NaNNaN86POLYGON 46.099441 49.386963 34.968903 58.878749 57.88955 64.757794 64.017263 53.26775 46.099441 49.386963NaNNaNPUBLICFalseNaN236870249700749778
7scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-2-473.56549460.128463imageRicker, George360635.75857360662.330557158.3999261000.0600.0NaN60697.0NaNNaN86POLYGON 64.289278 53.300712 58.27894 64.809113 87.684357 65.748281 84.289183 54.385971 64.289278 53.300712NaNNaNPUBLICFalseNaN236870333700758124
8scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-3-18.18414375.079718imageRicker, George360635.75711360662.328518158.3999261000.0600.0NaN60697.0NaNNaN86POLYGON 15.752091 67.29029 344.703486 70.125083 344.81612 82.345156 42.40894 75.855226 15.752091 67.29029NaNNaNPUBLICFalseNaN236870334700769279
9scienceTESSSPOCPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-3-261.19184483.924831imageRicker, George360635.75683260662.328817158.3999261000.0600.0NaN60697.0NaNNaN86POLYGON 42.971739 75.938329 344.850896 82.50859 164.325307 85.267937 95.996725 77.625463 42.971739 75.938329NaNNaNPUBLICFalseNaN236870335700778210
intentTypeobs_collectionprovenance_nameinstrument_nameprojectfilterswavelength_regiontarget_nametarget_classificationobs_ids_ras_decdataproduct_typeproposal_picalib_levelt_mint_maxt_exptimeem_minem_maxobs_titlet_obs_releaseproposal_idproposal_typesequence_numbers_regionjpegURLdataURLdataRightsmtFlagsrcDenobsidobjID
1408scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-361.74244134.4221imageRicker, George360635.75979360662.331209158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355NaNNaNPUBLICFalseNaN236870242700713800
1409scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-361.74244134.4221imageRicker, George360635.75979360662.331209158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355NaNNaNPUBLICFalseNaN236870242700713800
1410scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-361.74244134.4221imageRicker, George360635.75979360662.331209158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355NaNNaNPUBLICFalseNaN236870242700713800
1411scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-361.74244134.4221imageRicker, George360635.75979360662.331209158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355NaNNaNPUBLICFalseNaN236870242700713800
1412scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-361.74244134.4221imageRicker, George360635.75979360662.331209158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355NaNNaNPUBLICFalseNaN236870242700713800
1413scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-361.74244134.4221imageRicker, George360635.75979360662.331209158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355NaNNaNPUBLICFalseNaN236870242700713800
1414scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-361.74244134.4221imageRicker, George360635.75979360662.331209158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355NaNNaNPUBLICFalseNaN236870242700713800
1415scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-361.74244134.4221imageRicker, George360635.75979360662.331209158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355NaNNaNPUBLICFalseNaN236870242700713800
1416scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-361.74244134.4221imageRicker, George360635.75979360662.331209158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355NaNNaNPUBLICFalseNaN236870242700713800
1417scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFINaNtess-s0086-1-361.74244134.4221imageRicker, George360635.75979360662.331209158.399926600.01000.0NaN60697.0NaNNaN86POLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355NaNNaNPUBLICFalseNaN236870242700713800

Duplicate rows

Most frequently occurring

intentTypeobs_collectionprovenance_nameinstrument_nameprojectfilterswavelength_regiontarget_nameobs_ids_decdataproduct_typeproposal_picalib_levelt_mint_maxt_exptimeem_minem_maxt_obs_releasesequence_numbers_regiondataRightsmtFlagobsidobjID# duplicates
0scienceTESSSPOaPhotometerTESSTESSOpticalTESS FFItess-s0086-1-334.4221imageRicker, George360635.75979360662.331209158.399926600.01000.060697.086POLYGON 56.900467 27.410355 52.298245 38.232035 67.682461 41.457243 70.101446 29.709816 56.900467 27.410355PUBLICFalse23687024270071380011